A full-featured quantum computer will rely on some form of error correction as physical qubits are prone to the effects of environmental noise.
When using error correcting codes, decoders play a large role in the performance of the fault-tolerant protocols.
Using neural networks to decode the surface code has been suggested in earlier works [1]}, [2]}, [3]}, [4]}.
The primary motivation inspiring these works to use neural networks is their ability to adapt to the error model.
However, an issue that has not been addressed so far is scalability:
in previous approaches, the neural networks have to be re-trained for every system size, despite the fact that in general machine learning methods become problematic when the input space becomes too large.
Thus, it is interesting to ask whether there exists a family of quantum error correcting codes, which we can decode using neural networks in a clearly scalable way.
